Linking Spatial and Temporal Dynamic of Bacterioplankton Communities With Ecological Strategies Across a Coastal Frontal Area

Abstract

Bacterial Strategies Across a Front Tenacibaculum sp. or Pseudoalteromonas sp. reached their highest abundances during the more productive period. Overall, this study shows a remarkable coupling between bacterioplankton communities dynamics, trophic strategies, and seasonal cycles in a complex coastal environment.

Oceanographic structure and seasonal variation contribute to high heterogeneity in mesozooplankton over small spatial scales

“…Our results for copepod feeding traits are in general agreement with observations for bacterial communities associated with the Ushant Front. Copiotrophic bacteria that recycle detrital organic matter were also dominant in communities following the period of the phytoplankton blooms (Lemonnier et al, 2020). Both copepod and bacterial communities therefore appear to respond to increases in available organic matter post-bloom.…”

“…This front is known to increase primary production (Videau, 1987) but also to influence the size and abundance of phytoplankton (Pingree et al, 1975;Landeira et al, 2014), with longer diatom chains during the frontal establishment (Landeira et al, 2014). This coastal front also acts as an ecological boundary for free-living bacteria, with photosynthetic bacteria being most abundant offshore of the front, oligotrophic bacteria being more abundant in water masses with low phytoplankton and high inorganic nutrients, and opportunistic copiotrophic bacteria being most abundant in the most productive period of the year, associated with the front (Lemonnier et al, 2020). In addition, the mesozooplankton community showed its highest diversity at the front as a result of the cooccurrence of species from stratified and mixed waters (Schultes et al, 2013).…”

Seasonal niche differentiation among closely related marine bacteria

“…Further, investigating the dynamics of individual taxa —or finely resolved taxonomic units— on the short-term scale has revealed sharp turnover of communities mirroring environmental variability [ 11 ] and the relevance of interactions among microorganisms, influenced by the dynamics of phytoplankton blooms [ 12 , 13 ]. On longer time scales, these high-resolution analyses have shown recurrent co-varying taxa (modules) regardless of the interannual variation in phytoplankton blooms [ 14 ] or a clear partitioning of modules of oligotrophs and copiotrophs over time [ 15 ]. Nevertheless, these patterns of module covariance can be lost under contrasting environmental conditions, as shown by a recent study [ 16 ].…”

Spatiotemporal Heterogeneity and Intragenus Variability in Rhizobacterial Associations with Brassica rapa Growth

“…This resulted in networks that contained 423 to 787 ASVs ( Table 1 ). Associations between covarying ASVs were inferred with SPIEC-EASI using the “glasso” probabilistic inference method and a lambda.min.ratio of 0.01, and significant covariances were converted to correlations with the function cov2cor in a method similar to that of Lemonnier et al ( 73 ). To define modules of highly correlated ASVs, negative edges of networks were removed and the Louvain algorithm ( 74 ) was implemented using the cluster_louvain function in igraph ( 75 ).…”